Demo

Live Web App Demo Link

Live Web App

Deployment on Heroku: http://mlcricketscoreprediction.herokuapp.com/

Abstract

The purpose of this report will be to use the IPL.csv Data to predict the score of cricket match.

This can be used to gain insight into how and why cricket score varies depending on the wickets and runs etc. This can also be used as a model to gain a marketing advantage, by advertisement targeting those who are more likely to win cricket match and which team has more chances to lose. Score Prediction is a regression problem, where using the match data and model building will predict what range of score a team can achieve.

This is diving into Prediction of Cricket Score through Machine Learning Concept. End to End Project means that it is step by step process, starts with data collection, EDA, Data Preparation which includes cleaning and transforming then selecting, training and saving ML Models, Cross-validation and Hyper-Parameter Tuning and developing web service then Deployment. This repository contains the code for Predicting Cricket Score using python’s various libraries.

It used pandas, sklearn, datetime and pickle libraries. These libraries help to perform individually one particular functionality. Pandas objects rely heavily on Numpy objects. Sklearn has 100 to 200 models. “Pickling” is the process whereby a Python object hierarchy is converted into a byte stream. Datetime module supplies classes to work with date and time. The purpose of creating this repository is to gain insights into Complete ML Project. These python libraries raised knowledge in discovering these libraries with practical use of it. It leads to growth in my ML repository. This above screenshot and Video_File Folder will help you to understand flow of output.

Motivation

The reason behind building this is, because till now I have worked on individual concepts so I wanted to combine all things that I have learnt till now and create a End to End Project that shows whole life cycle of ML Project. In addition to that, as an employee of a company, I should be able to carry out an entire process own is also essential aspect of it. Building end to end project is gave me wholesome approach to handle given data. Hence, I continue to gain knowledge while practicing the same and spread literary wings in tech-heaven.

Acknowledgment

Dataset Available here

  Dataset Available: https://www.kaggle.com/yuvrajdagur/ipl-dataset-season-2008-to-2017 

The Data

Here are all the features included in the data set, and a short description of them all.

It has 76014 total observations and 15 columns.

This data has been cleaned of any null values.

Analysis of the Data

Let’s start by doing a general analysis of the data as a whole, including all the features the Linear Regression algorithm will be using.

Modelling

import pandas as pd
import pickle   

df = pd.read_csv('ipl.csv')

columns_to_remove = ['mid', 'venue', 'batsman', 'bowler', 'striker', 'non-striker']
df.drop(labels=columns_to_remove, axis=1, inplace=True)
consistent_teams = ['Kolkata Knight Riders', 'Chennai Super Kings', 'Rajasthan Royals',
                    'Mumbai Indians', 'Kings XI Punjab', 'Royal Challengers Bangalore',
                    'Delhi Daredevils', 'Sunrisers Hyderabad']
df = df[(df['bat_team'].isin(consistent_teams)) & (df['bowl_team'].isin(consistent_teams))]
df = df[df['overs']>=5.0]
from datetime import datetime
df['date'] = df['date'].apply(lambda x: datetime.strptime(x, '%Y-%m-%d'))      

encoded_df = pd.get_dummies(data=df, columns=['bat_team', 'bowl_team'])
encoded_df = encoded_df[['date', 'bat_team_Chennai Super Kings', 'bat_team_Delhi Daredevils', 'bat_team_Kings XI Punjab',
              'bat_team_Kolkata Knight Riders', 'bat_team_Mumbai Indians', 'bat_team_Rajasthan Royals',
              'bat_team_Royal Challengers Bangalore', 'bat_team_Sunrisers Hyderabad',
              'bowl_team_Chennai Super Kings', 'bowl_team_Delhi Daredevils', 'bowl_team_Kings XI Punjab',
              'bowl_team_Kolkata Knight Riders', 'bowl_team_Mumbai Indians', 'bowl_team_Rajasthan Royals',
              'bowl_team_Royal Challengers Bangalore', 'bowl_team_Sunrisers Hyderabad',
              'overs', 'runs', 'wickets', 'runs_last_5', 'wickets_last_5', 'total']]    

X_train = encoded_df.drop(labels='total', axis=1)[encoded_df['date'].dt.year <= 2016]
X_test = encoded_df.drop(labels='total', axis=1)[encoded_df['date'].dt.year >= 2017]

y_train = encoded_df[encoded_df['date'].dt.year <= 2016]['total'].values
y_test = encoded_df[encoded_df['date'].dt.year >= 2017]['total'].values
X_train.drop(labels='date', axis=True, inplace=True)
X_test.drop(labels='date', axis=True, inplace=True)       

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,y_train)       

filename = 'first-innings-score-lr-model.pkl'
pickle.dump(regressor, open(filename, 'wb'))   

from flask import Flask, render_template, request
import pickle
import numpy as np
filename = 'first-innings-score-lr-model.pkl'
regressor = pickle.load(open(filename, 'rb'))

app = Flask(__name__)

@app.route('/')
def home():
	return render_template('index.html')

@app.route('/predict', methods=['POST'])
def predict():
    temp_array = list()
    
    if request.method == 'POST':
        
        batting_team = request.form['batting-team']
        if batting_team == 'Chennai Super Kings':
            temp_array = temp_array + [1,0,0,0,0,0,0,0]
        elif batting_team == 'Delhi Daredevils':
            temp_array = temp_array + [0,1,0,0,0,0,0,0]
        elif batting_team == 'Kings XI Punjab':
            temp_array = temp_array + [0,0,1,0,0,0,0,0]
        elif batting_team == 'Kolkata Knight Riders':
            temp_array = temp_array + [0,0,0,1,0,0,0,0]
        elif batting_team == 'Mumbai Indians':
            temp_array = temp_array + [0,0,0,0,1,0,0,0]
        elif batting_team == 'Rajasthan Royals':
            temp_array = temp_array + [0,0,0,0,0,1,0,0]
        elif batting_team == 'Royal Challengers Bangalore':
            temp_array = temp_array + [0,0,0,0,0,0,1,0]
        elif batting_team == 'Sunrisers Hyderabad':
            temp_array = temp_array + [0,0,0,0,0,0,0,1]
            
            
        bowling_team = request.form['bowling-team']
        if bowling_team == 'Chennai Super Kings':
            temp_array = temp_array + [1,0,0,0,0,0,0,0]
        elif bowling_team == 'Delhi Daredevils':
            temp_array = temp_array + [0,1,0,0,0,0,0,0]
        elif bowling_team == 'Kings XI Punjab':
            temp_array = temp_array + [0,0,1,0,0,0,0,0]
        elif bowling_team == 'Kolkata Knight Riders':
            temp_array = temp_array + [0,0,0,1,0,0,0,0]
        elif bowling_team == 'Mumbai Indians':
            temp_array = temp_array + [0,0,0,0,1,0,0,0]
        elif bowling_team == 'Rajasthan Royals':
            temp_array = temp_array + [0,0,0,0,0,1,0,0]
        elif bowling_team == 'Royal Challengers Bangalore':
            temp_array = temp_array + [0,0,0,0,0,0,1,0]
        elif bowling_team == 'Sunrisers Hyderabad':
            temp_array = temp_array + [0,0,0,0,0,0,0,1]
            
            
        overs = float(request.form['overs'])
        runs = int(request.form['runs'])
        wickets = int(request.form['wickets'])
        runs_in_prev_5 = int(request.form['runs_in_prev_5'])
        wickets_in_prev_5 = int(request.form['wickets_in_prev_5'])
        
        temp_array = temp_array + [overs, runs, wickets, runs_in_prev_5, wickets_in_prev_5]
        
        data = np.array([temp_array])
        my_prediction = int(regressor.predict(data)[0])
              
        return render_template('result.html', lower_limit = my_prediction-10, upper_limit = my_prediction+5)



if __name__ == '__main__':
	app.run(debug=True)       

Creation of App

Here, I am creating Flask App. Loading the model that we saved then calling and calculating all the factors that are necessary in deciding score of matches. Get the user entered value from the predict function and render respective .html page for solution. You can create it with Streamlit also.

Technical Aspect

Pandas module mainly works with the tabular data. It contains Data Frame and Series. Pandas is 18 to 20 times slower than Numpy. Pandas is seriously a game changer when it comes to cleaning, transforming, manipulating and analyzing data.

Sklearn is known as scikit learn. It provides many ML libraries and algorithms for it. It provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python.

Pickle in Python is primarily used in serializing and deserializing a Python object structure. In other words, it's the process of converting a Python object into a byte stream to store it in a file/database, maintain program state across sessions, or transport data over the network.

Need to train_test_split - Using the same dataset for both training and testing leaves room for miscalculations, thus increases the chances of inaccurate predictions. The train_test_split function allows you to break a dataset with ease while pursuing an ideal model. Also, keep in mind that your model should not be overfitting or underfitting.

Date and datetime are an object in Python, so when you manipulate them, you are actually manipulating objects and not string or timestamps. Working with dates and times is one of the biggest challenges in programming. Between dealing with time zones, daylight saving time, and different written date formats, it can be tough to keep track of which days and times you’re referencing. Fortunately, the built-in Python datetime module can help you manage the complex nature of dates and times. For example, one great example of this irregularity is daylight saving time. In the United States and Canada, clocks are set forward by one hour on the second Sunday in March and set back by one hour on the first Sunday in November. However, this has only been the case since 2007. Prior to 2007, clocks were set forward on the first Sunday in April and set back on the last Sunday in October. Things get even more complicated when you consider time zones. Ideally, time zone boundaries would follow lines of longitude exactly.

Linear regression is the next step up after correlation. It is used when we want to predict the value of a variable based on the value of another variable. The variable we want to predict is called the dependent variable. Because the model is based on the equation of a straight line, y=a+bx, where a is the y-intercept (the value of y when x=0) and b is the slope (the degree to which y increases as x increases one unit). Linear regression plots a straight line through a y vs. x scatterplot. That is why it is call linear regression. Simple linear regression is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables: One variable, denoted x, is regarded as the predictor, explanatory, or independent variable. The goal of multiple linear regression (MLR) is to model the linear relationship between the explanatory (independent) variables and response (dependent) variable. In essence, multiple regression is the extension of ordinary least-squares (OLS) regression that involves more than one explanatory variable.

Installation

Using intel core i5 9th generation with NVIDIA GFORECE GTX1650.

Windows 10 Environment Used.

Already Installed Anaconda Navigator for Python 3.x

The Code is written in Python 3.8.

If you don't have Python installed you can install Python from its official site.

If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip, python -m pip install --upgrade pip and press Enter.

Run-How to Use-Steps

Keep your internet connection on while running or accessing files and throughout too.

Follow this when you want to perform from scratch.

Open Anaconda Prompt, Perform the following steps:

cd

pip install pandas

pip install sklearn

pip install numpy

pip install scipy

pip install matplotlib

Note: If it shows error as ‘No Module Found’ , then install relevant module.

You can also create requirement.txt file as, pip freeze > requirements.txt

Create Virtual Environment:

conda create -n ipl python=3.6

y

conda activate ipl

cd

run .py files.

Paste URL to browser to check whether working locally or not.

Follow this when you want to just perform on local machine.

Download ZIP File.

Right-Click on ZIP file in download section and select Extract file option, which will unzip file.

Move unzip folder to desired folder/location be it D drive or desktop etc.

Open Anaconda Prompt, write cd and press Enter.

eg: cd C:\Users\Monica\Desktop\Projects\Python Projects 1\ 23)End_To_End_Projects\ Project_6_ML_FileUse_EndToEnd_Cricket_IPL_FirstInningsScorePrediction\Project_ML_CricketScorePrediction

conda create -n ipl python=3.7

y

conda activate ipl

In Anconda Prompt, pip install -r requirements.txt to install all packages.

In Anconda Prompt, write python app.py and press Enter.

Paste URL to browser to check whether working locally or not.

Please be careful with spellings or numbers while typing filename and easier is just copy filename and then run it to avoid any silly errors.

Note: cd

[Go to Folder where file is. Select the path from top and right-click and select copy option and paste it next to cd one space and press enter, then you can access all files of that folder] [cd means change directory]

Directory Tree-Structure of Project

To Do-Future Scope

Can create app for tennis.

Add columns in dataset of top batsmen and bowlers of all the teams.

Add columns that consists of striker and non-striker's strike rates.

Can deploy on AWS.

Technologies Used/System Requirements/Tech Stack

Download the Material

Conclusion

Credits

Krish Naik Channel

https://machinelearningmastery.com/linear-regression-for-machine-learning/

Paper Citation

  Paper Citation here